Generating ClickHouse query performance reports

This skill is the methodology for investigating slow ClickHouse queries and writing up a performance report. It pairs with [query-clickhouse-via-metabase](../query-clickhouse-via-metabase/SKILL.md), which is the mechanism (SSO-gated auth and hogli metabase:query). Run every query in this skill through that one.

Reports themselves are not public. When it exists, the private PostHog/query-performance-analysis repo holds the historical reports and example query IDs; this repo holds only the tooling and methodology. That repo is usually checked out as a sibling folder to the posthog checkout (e.g. ../query-performance-analysis relative to the repo root, or alongside it under the same parent directory). Look for a sibling directory named query-performance-analysis containing an analysis/ folder of dated reports. If you find it, add the new report there as a new markdown file under analysis/, named <YYYY-MM-DD>-<topic>.md (match the existing naming, e.g. 2026-05-27-slow-queries-14d.md).

The sibling repo may not exist, and that is fine. If you cannot find it, do not write into the public posthog repo and do not block on it: write the report to a temp folder instead (e.g. /tmp/<YYYY-MM-DD>-<topic>.md), tell the user where you put it, and skip the previous-report comparison in step 9 (there is no history to diff against).

Data source: posthog.querylogarchive (not system.query_log)

system.query_log on the production clusters retains only a few hours, so it cannot answer a multi-day question. Use the Distributed archive table instead:

FROM posthog.query_log_archive

It retains roughly three weeks and exposes logcomment as typed columns, so you skip JSONExtract. Query it directly (it already fans out across the cluster). Always filter isinitial_query so distributed sub-queries are not double-counted. Confirm current retention with a per-day count() before trusting a window (see references/query-patterns.md).

Key columns (full list via system.columns WHERE table='querylogarchive'):

Both regions have the archive. US and EU are separate clusters with different workloads and materialized columns; run cross-region comparisons against both. Discover the current ClickHouse database id per region with hogli metabase:databases (ids are not stable). Note that the ONLINE and OFFLINE Metabase connections for a region fan out to the same logical cluster, so they return the same querylogarchive data.

What counts as a slow query

query_duration_ms > 30000 OR exception_code IN (159, 160, 241)

Do not add type = 'QueryFinish': OOM and timeout rows are type = 'ExceptionWhileProcessing', so that filter silently drops every failure. The duration/exception predicate already excludes QueryStart rows (duration 0). Exclude the cluster health-poll query by normalizedqueryhash (pattern in references/query-patterns.md).

Producing the report

The standard workflow, building from coarse to specific. Each step's SQL is in references/query-patterns.md.

Do not read previous reports until step 9. Steps 1-8 should run against the raw data with fresh eyes, so the analysis captures the largest surface area rather than re-walking last report's findings. Reading the prior report early anchors you to its categories and makes it easy to miss a new problem it never mentioned. Diff against history only after the independent pass is done.

1. Confirm the window. Per-day count() over the intended range to verify the archive actually

covers it (retention can be shorter than you expect).

1. Headline summary. Total slow queries, total cluster query-hours, bytes read, teams touched,

and the split across succeeded-but-slow / timeouts / OOMs / other. Also capture the cluster-wide totals across all queries (not just the slow set): total query-seconds, total CPU-seconds (typed ProfileEvents_OSCPUVirtualTimeMicroseconds column, not the Map lookup), total bytes read, and total OOMs (references/query-patterns.md §1b). The slow-set sums are a biased subset; the all-query totals are the honest "busier / reading more this period?" denominator and the baseline future reports diff against. They cannot be backfilled once a window ages past retention, so record them every run.

1. Date distribution. Slow count, timeouts, and OOMs per day. This is where incidents announce

themselves: a multi-day OOM or timeout surge against a flat baseline.

1. Categorize. Group by lckind × lcproduct × lcaccessmethod. This separates background

work (data modeling, dagster pre-aggregation, batch exports) from synchronous user-facing queries.

1. Attribute. Drill into the worst categories by team_id. Rank by total cluster-hours

(sum(querydurationms)) and by OOM count separately. Before calling anything systemic, check whether one team or one API key dominates a metric: a single integration querying via a personalapikey can account for the large majority of cluster OOMs, and the "incident" is then really one tenant. Attribute by teamid + lcapikeylabel first. Then add a top-consumers view over all queries (not just the slow set): top teams, top API keys (lcapikeylabel), and top tools (lcproduct) ranked by bytes, CPU-seconds, and wall-time (references/query-patterns.md §4c). This is where the heavy-but-fast consumers show up: a tenant or integration can dominate cluster CPU or bytes through millions of cheap queries while never crossing the slow threshold, so it is invisible to the slow-set ranking. The CPU:wall ratio per row separates compute-bound from wait/IO-bound load.

1. Characterize user-facing slowness. For lckind='request' AND lcproduct='product_analytics'

with empty lcaccessmethod (logged-in web), break down by lc_query__kind and flag breakdownvalue usage and JSONExtract over personproperties. This is the product-actionable bucket. Always include the JSON-extracted property breakdown (references/query-patterns.md §7): the top event vs person property names pulled from JSON blobs in the slow set, and which teams use each. These are the materialization candidates and a required report output. HogQLQuery (arbitrary user- and AI-authored SQL) deserves its own deep dive, including how much is AI-written and why it is slow; see references/hogql-deep-dive.md.

1. Root-cause the worst offenders. For the top findings, do not stop at "team X is slow": pull the

full query and form a hypothesis for why, then test it with EXPLAIN. Root-causing an individual query is the [optimizing-clickhouse-and-hogql-queries](../optimizing-clickhouse-and-hogql-queries/SKILL.md) skill's job; its [references/investigation-playbook.md](../optimizing-clickhouse-and-hogql-queries/references/investigation-playbook.md) is the playbook (pull the full query, bytes vs CPU vs duration, the runtime causes, origin tracing, EXPLAIN). A useful finding includes a why ("scans full history because the time filter is function-wrapped and can't prune granules"), even if stated as a hypothesis.

1. Examples + write-up. Capture queryid + eventdate for the worst offenders in each finding,

then write the report (structure below). Because system.querylog retention is short, examples are resolved from querylogarchive (WHERE queryid = '…' AND eventdate = '…'), not the old Metabase lookup card. Link each example to a shareable self-contained Metabase URL (the querylink recipe in references/query-patterns.md) so a reader clicks straight through to the query. When you draft the recommendations, ground the researchable ones in code by spawning background research agents (see "Grounding recommendations in code" below) so a recommendation points at the actual file and change rather than saying "audit X".

1. Diff against the previous report (do this last, if there is one). If the sibling

query-performance-analysis repo is not present, skip this step entirely. Otherwise, only now, after the independent pass above, read the most recent dated report in its analysis/ folder (sort by filename date). Add a short delta section to the new report covering: what moved since last time (new incidents, findings that grew or resolved, headline numbers up or down), and a follow-up check on anything the previous report flagged as needing action (a materialization that was recommended, a team to watch, a pipeline to make incremental). For each prior follow-up, state whether it is resolved, still open, or regressed, with the current numbers as evidence. Doing this last is deliberate: it keeps the fresh analysis unbiased while still closing the loop on history. Make the windows comparable before quoting a delta: confirm the previous report used the same window length (both reports here use a trailing now() - INTERVAL N DAY, so equal length but with overlapping and partial edge days). Headline totals between two trailing windows are usually dominated by whichever one-off incident sits inside one window and not the other, so a large drop is rarely a structural improvement. Always also compare an incident-excluded baseline (e.g. OOMs/day with the spike days removed) so the delta is not misread, and say explicitly when a total moved because an incident aged into or out of the window. Remember the summed metrics (bytes read, cluster-hours) cover the slow set only, not total cluster I/O, so they also move when a heavy background job's runs cross or stop crossing the 30s threshold; attribute a big bytes/hours swing to specific categories (it is usually one or two background pipelines) rather than reporting it as a cluster-wide change.

Grounding recommendations in code

A recommendation like "audit pipeline X" or "materialize property Y" is far more useful when it points at the actual code. For each recommendation that maps to a concrete place in the PostHog codebase, spawn a background research agent (the Agent tool, runinbackground: true, subagent_type: general-purpose or Explore) to read the source and return: how the relevant code works today, the specific file / function to change, any constraints, and whether a better mechanism already exists. Spawn one agent per researchable recommendation, all in a single message so they run in parallel, as soon as the recommendations are drafted. Let them run while you do the delta (step 9) and finalize the write-up, then fold each finding into its recommendation: replace "audit X" with "X is implemented in <file> as <current behavior>; the change is <specific>", and cite the file paths so the human can jump straight in. The agents research and report only; they do not change code.

Not every recommendation is researchable this way. Spawn an agent only where source code is the source of truth; skip operational / infra items:

Give each agent a focused prompt: the recommendation, the specific question, and an instruction to return file paths + current behavior + the precise change point and to change nothing. The agents read the posthog repo (where this skill lives); the report itself is written to the separate query-performance-analysis repo.

Interpreting the results

• Two populations live in "slow queries." Tight-timeout API noise (queries erroring at ~10s

against a low maxexecutiontime, usually personalapikey) inflates the raw count without representing real compute. Genuinely expensive work is better measured by total cluster-hours and OOM count. Always call this distinction out; do not let timeout volume masquerade as slowness.

• Bytes read is the truest cost signal, more than duration (which varies with cache and cluster

load). High bytes against low rows means heavy columns, almost always JSONExtract over a properties blob. For root-causing individual queries, see the [optimizing-clickhouse-and-hogql-queries](../optimizing-clickhouse-and-hogql-queries/SKILL.md) skill.

• Background pipelines usually dominate raw cluster-time (data-modeling DAGs, web-analytics

pre-aggregation). That is expected; weigh them by whether their scan volume is necessary, separately from user-facing latency.

Report structure

A report should contain, in order:

1. One-line scope: region, window, and the slow definition / exclusions used.
2. Headline numbers table + the cluster-wide totals (all queries) table (total query-seconds,

CPU-seconds, bytes read, OOMs) + the two-populations caveat.

1. Daily distribution table (flag any incident window).
2. Findings, worst first. **Every finding needs at least one concrete queryid + eventdate,

linked via the shareable query_link URL (see references/query-patterns.md) so a reader clicks straight through to the exact query, plus a hypothesis for why it is slow** (from the [optimizing-clickhouse-and-hogql-queries](../optimizing-clickhouse-and-hogql-queries/SKILL.md) skill's investigation playbook). Group findings by what they are: a per-tenant incident, the heaviest cluster-time consumers, user-facing insight slowness, and tight-timeout API noise.

1. A top-consumers-by-resource section (all queries, not just slow): top teams, top API keys, and

top tools (lc_product) ranked by bytes / CPU / wall-time (references/query-patterns.md §4c), calling out consumers that never trip the slow threshold and the compute-bound vs wait-bound split.

1. A JSON-extracted property table: the top event and person property names pulled from JSON blobs

in the slow set, with the teams using each (references/query-patterns.md §7). These are the materialization candidates.

1. Concrete recommendations tied to each finding (materialize property X, cap memory per API key,

make pipeline Y incremental, ...). Ground the researchable ones in code (see "Grounding recommendations in code"): cite the file / function and the specific change, not just "audit X".

1. A delta vs the previous report (step 9): what changed since last time, plus a follow-up check on

each action the previous report recommended (resolved / still open / regressed, with numbers). Omit this section when there is no previous report.

Save the finished report as analysis/<YYYY-MM-DD>-<topic>.md in the sibling query-performance-analysis repo, never in the public posthog repo; if that repo is not present, save to a temp folder (e.g. /tmp/<YYYY-MM-DD>-<topic>.md) and tell the user the path.

References

• references/query-patterns.md: ready-to-run SQL for every step above, against querylogarchive.
• references/materialization-analysis.md: finding properties to materialize and columns to drop,

run across both US and EU.

• references/hogql-deep-dive.md: analyzing HogQLQuery (arbitrary user/AI SQL) specifically,

including how to identify AI-written HogQL (lcproduct/lcfeature, not aiquerysource) and the causes that make ad-hoc and AI queries slow.

Related skills

This skill is fleet-level: it finds and ranks slow queries across all teams and writes the report. Once a finding points at one query you want to explain or fix, switch to [optimizing-clickhouse-and-hogql-queries](../optimizing-clickhouse-and-hogql-queries/SKILL.md) — it owns root-causing an individual query (its references/investigation-playbook.md) and applying the fix at the right layer (printer, query runner, or ClickHouse migration).